839 lines
25 KiB
Python
839 lines
25 KiB
Python
#
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#
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# 0=================================0
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# | Kernel Point Convolutions |
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# 0=================================0
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#
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#
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Callable script to test any model on any dataset
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#
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Hugues THOMAS - 11/06/2018
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#
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Imports and global variables
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# \**********************************/
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#
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# Common libs
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import torch
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import numpy as np
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import matplotlib.pyplot as plt
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from os.path import isfile, join, exists
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from os import listdir, remove
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# My libs
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from utils.config import Config
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from utils.metrics import IoU_from_confusions, smooth_metrics, fast_confusion
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from utils.ply import read_ply
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# Datasets
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from datasetss.S3DIS import S3DISDataset
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from datasetss.SemanticKitti import SemanticKittiDataset
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Utility functions
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# \***********************/
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#
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def listdir_str(path):
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# listdir can return binary string instead od decoded string sometimes.
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# This function ensures a steady behavior
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f_list = []
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for f in listdir(path):
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try:
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f = f.decode()
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except (UnicodeDecodeError, AttributeError):
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pass
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f_list.append(f)
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return f_list
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def running_mean(signal, n, axis=0, stride=1):
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signal = np.array(signal)
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torch_conv = torch.nn.Conv1d(1, 1, kernel_size=2 * n + 1, stride=stride, bias=False)
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torch_conv.weight.requires_grad_(False)
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torch_conv.weight *= 0
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torch_conv.weight += 1 / (2 * n + 1)
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if signal.ndim == 1:
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torch_signal = torch.from_numpy(signal.reshape([1, 1, -1]).astype(np.float32))
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return torch_conv(torch_signal).squeeze().numpy()
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elif signal.ndim == 2:
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print("TODO implement with torch and stride here")
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smoothed = np.empty(signal.shape)
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if axis == 0:
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for i, sig in enumerate(signal):
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sig_sum = np.convolve(sig, np.ones((2 * n + 1,)), mode="same")
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sig_num = np.convolve(sig * 0 + 1, np.ones((2 * n + 1,)), mode="same")
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smoothed[i, :] = sig_sum / sig_num
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elif axis == 1:
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for i, sig in enumerate(signal.T):
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sig_sum = np.convolve(sig, np.ones((2 * n + 1,)), mode="same")
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sig_num = np.convolve(sig * 0 + 1, np.ones((2 * n + 1,)), mode="same")
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smoothed[:, i] = sig_sum / sig_num
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else:
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print("wrong axis")
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return smoothed
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else:
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print("wrong dimensions")
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return None
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def IoU_class_metrics(all_IoUs, smooth_n):
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# Get mean IoU per class for consecutive epochs to directly get a mean without further smoothing
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smoothed_IoUs = []
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for epoch in range(len(all_IoUs)):
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i0 = max(epoch - smooth_n, 0)
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i1 = min(epoch + smooth_n + 1, len(all_IoUs))
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smoothed_IoUs += [np.mean(np.vstack(all_IoUs[i0:i1]), axis=0)]
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smoothed_IoUs = np.vstack(smoothed_IoUs)
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smoothed_mIoUs = np.mean(smoothed_IoUs, axis=1)
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return smoothed_IoUs, smoothed_mIoUs
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def load_confusions(filename, n_class):
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with open(filename, "r") as f:
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lines = f.readlines()
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confs = np.zeros((len(lines), n_class, n_class))
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for i, line in enumerate(lines):
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C = np.array([int(value) for value in line.split()])
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confs[i, :, :] = C.reshape((n_class, n_class))
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return confs
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def load_training_results(path):
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filename = join(path, "training.txt")
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with open(filename, "r") as f:
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lines = f.readlines()
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epochs = []
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steps = []
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L_out = []
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L_p = []
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acc = []
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t = []
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for line in lines[1:]:
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line_info = line.split()
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if len(line) > 0:
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epochs += [int(line_info[0])]
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steps += [int(line_info[1])]
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L_out += [float(line_info[2])]
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L_p += [float(line_info[3])]
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acc += [float(line_info[4])]
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t += [float(line_info[5])]
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else:
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break
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return epochs, steps, L_out, L_p, acc, t
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def load_single_IoU(filename, n_parts):
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with open(filename, "r") as f:
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lines = f.readlines()
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# Load all IoUs
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all_IoUs = []
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for i, line in enumerate(lines):
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all_IoUs += [np.reshape([float(IoU) for IoU in line.split()], [-1, n_parts])]
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return all_IoUs
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def load_snap_clouds(path, dataset, only_last=False):
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cloud_folders = np.array(
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[join(path, f) for f in listdir_str(path) if f.startswith("val_preds")]
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)
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cloud_epochs = np.array([int(f.split("_")[-1]) for f in cloud_folders])
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epoch_order = np.argsort(cloud_epochs)
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cloud_epochs = cloud_epochs[epoch_order]
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cloud_folders = cloud_folders[epoch_order]
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Confs = np.zeros(
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(len(cloud_epochs), dataset.num_classes, dataset.num_classes), dtype=np.int32
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)
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for c_i, cloud_folder in enumerate(cloud_folders):
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if only_last and c_i < len(cloud_epochs) - 1:
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continue
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# Load confusion if previously saved
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conf_file = join(cloud_folder, "conf.txt")
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if isfile(conf_file):
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Confs[c_i] += np.loadtxt(conf_file, dtype=np.int32)
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else:
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for f in listdir_str(cloud_folder):
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if f.endswith(".ply") and not f.endswith("sub.ply"):
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data = read_ply(join(cloud_folder, f))
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labels = data["class"]
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preds = data["preds"]
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Confs[c_i] += fast_confusion(
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labels, preds, dataset.label_values
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).astype(np.int32)
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np.savetxt(conf_file, Confs[c_i], "%12d")
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# Erase ply to save disk memory
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if c_i < len(cloud_folders) - 1:
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for f in listdir_str(cloud_folder):
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if f.endswith(".ply"):
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remove(join(cloud_folder, f))
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# Remove ignored labels from confusions
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for l_ind, label_value in reversed(list(enumerate(dataset.label_values))):
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if label_value in dataset.ignored_labels:
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Confs = np.delete(Confs, l_ind, axis=1)
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Confs = np.delete(Confs, l_ind, axis=2)
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return cloud_epochs, IoU_from_confusions(Confs)
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Plot functions
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# \********************/
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#
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def compare_trainings(list_of_paths, list_of_labels=None):
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# Parameters
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# **********
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plot_lr = False
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smooth_epochs = 0.5
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stride = 2
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if list_of_labels is None:
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list_of_labels = [str(i) for i in range(len(list_of_paths))]
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# Read Training Logs
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# ******************
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all_epochs = []
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all_loss = []
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all_lr = []
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all_times = []
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for path in list_of_paths:
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print(path)
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if ("val_IoUs.txt" in [f for f in listdir_str(path)]) or (
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"val_confs.txt" in [f for f in listdir_str(path)]
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):
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config = Config()
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config.load(path)
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else:
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continue
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# Load results
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epochs, steps, L_out, L_p, acc, t = load_training_results(path)
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epochs = np.array(epochs, dtype=np.int32)
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epochs_d = np.array(epochs, dtype=np.float32)
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steps = np.array(steps, dtype=np.float32)
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# Compute number of steps per epoch
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max_e = np.max(epochs)
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first_e = np.min(epochs)
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epoch_n = []
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for i in range(first_e, max_e):
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bool0 = epochs == i
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e_n = np.sum(bool0)
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epoch_n.append(e_n)
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epochs_d[bool0] += steps[bool0] / e_n
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smooth_n = int(np.mean(epoch_n) * smooth_epochs)
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smooth_loss = running_mean(L_out, smooth_n, stride=stride)
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all_loss += [smooth_loss]
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all_epochs += [epochs_d[smooth_n:-smooth_n:stride]]
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all_times += [t[smooth_n:-smooth_n:stride]]
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# Learning rate
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if plot_lr:
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lr_decay_v = np.array([lr_d for ep, lr_d in config.lr_decays.items()])
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lr_decay_e = np.array([ep for ep, lr_d in config.lr_decays.items()])
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max_e = max(np.max(all_epochs[-1]) + 1, np.max(lr_decay_e) + 1)
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lr_decays = np.ones(int(np.ceil(max_e)), dtype=np.float32)
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lr_decays[0] = float(config.learning_rate)
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lr_decays[lr_decay_e] = lr_decay_v
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lr = np.cumprod(lr_decays)
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all_lr += [lr[np.floor(all_epochs[-1]).astype(np.int32)]]
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# Plots learning rate
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# *******************
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if plot_lr:
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# Figure
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fig = plt.figure("lr")
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for i, label in enumerate(list_of_labels):
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plt.plot(all_epochs[i], all_lr[i], linewidth=1, label=label)
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# Set names for axes
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plt.xlabel("epochs")
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plt.ylabel("lr")
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plt.yscale("log")
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# Display legends and title
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plt.legend(loc=1)
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# Customize the graph
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ax = fig.gca()
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ax.grid(linestyle="-.", which="both")
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# ax.set_yticks(np.arange(0.8, 1.02, 0.02))
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# Plots loss
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# **********
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# Figure
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fig = plt.figure("loss")
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for i, label in enumerate(list_of_labels):
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plt.plot(all_epochs[i], all_loss[i], linewidth=1, label=label)
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# Set names for axes
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plt.xlabel("epochs")
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plt.ylabel("loss")
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plt.yscale("log")
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# Display legends and title
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plt.legend(loc=1)
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plt.title("Losses compare")
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# Customize the graph
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ax = fig.gca()
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ax.grid(linestyle="-.", which="both")
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# ax.set_yticks(np.arange(0.8, 1.02, 0.02))
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# Plot Times
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# **********
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# Figure
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fig = plt.figure("time")
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for i, label in enumerate(list_of_labels):
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plt.plot(all_epochs[i], np.array(all_times[i]) / 3600, linewidth=1, label=label)
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# Set names for axes
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plt.xlabel("epochs")
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plt.ylabel("time")
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# plt.yscale('log')
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# Display legends and title
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plt.legend(loc=0)
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# Customize the graph
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ax = fig.gca()
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ax.grid(linestyle="-.", which="both")
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# ax.set_yticks(np.arange(0.8, 1.02, 0.02))
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# Show all
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plt.show()
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def compare_convergences_segment(dataset, list_of_paths, list_of_names=None):
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# Parameters
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# **********
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smooth_n = 10
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if list_of_names is None:
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list_of_names = [str(i) for i in range(len(list_of_paths))]
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# Read Logs
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# *********
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all_pred_epochs = []
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all_mIoUs = []
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all_class_IoUs = []
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all_snap_epochs = []
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all_snap_IoUs = []
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# Load parameters
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config = Config()
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config.load(list_of_paths[0])
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class_list = [
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dataset.label_to_names[label]
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for label in dataset.label_values
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if label not in dataset.ignored_labels
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]
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s = "{:^10}|".format("mean")
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for c in class_list:
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s += "{:^10}".format(c)
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print(s)
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print(10 * "-" + "|" + 10 * config.num_classes * "-")
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for path in list_of_paths:
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# Get validation IoUs
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file = join(path, "val_IoUs.txt")
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val_IoUs = load_single_IoU(file, config.num_classes)
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# Get mean IoU
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class_IoUs, mIoUs = IoU_class_metrics(val_IoUs, smooth_n)
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# Aggregate results
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all_pred_epochs += [np.array([i for i in range(len(val_IoUs))])]
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all_mIoUs += [mIoUs]
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all_class_IoUs += [class_IoUs]
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s = "{:^10.1f}|".format(100 * mIoUs[-1])
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for IoU in class_IoUs[-1]:
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s += "{:^10.1f}".format(100 * IoU)
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print(s)
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# Get optional full validation on clouds
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snap_epochs, snap_IoUs = load_snap_clouds(path, dataset)
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all_snap_epochs += [snap_epochs]
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all_snap_IoUs += [snap_IoUs]
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print(10 * "-" + "|" + 10 * config.num_classes * "-")
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for snap_IoUs in all_snap_IoUs:
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if len(snap_IoUs) > 0:
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s = "{:^10.1f}|".format(100 * np.mean(snap_IoUs[-1]))
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for IoU in snap_IoUs[-1]:
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s += "{:^10.1f}".format(100 * IoU)
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else:
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s = "{:^10s}".format("-")
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for _ in range(config.num_classes):
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s += "{:^10s}".format("-")
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print(s)
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# Plots
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# *****
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# Figure
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fig = plt.figure("mIoUs")
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for i, name in enumerate(list_of_names):
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p = plt.plot(all_pred_epochs[i], all_mIoUs[i], "--", linewidth=1, label=name)
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plt.plot(
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all_snap_epochs[i],
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np.mean(all_snap_IoUs[i], axis=1),
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linewidth=1,
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color=p[-1].get_color(),
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)
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plt.xlabel("epochs")
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plt.ylabel("IoU")
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# Set limits for y axis
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# plt.ylim(0.55, 0.95)
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# Display legends and title
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plt.legend(loc=4)
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# Customize the graph
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ax = fig.gca()
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ax.grid(linestyle="-.", which="both")
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# ax.set_yticks(np.arange(0.8, 1.02, 0.02))
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displayed_classes = [0, 1, 2, 3, 4, 5, 6, 7]
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displayed_classes = []
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for c_i, c_name in enumerate(class_list):
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if c_i in displayed_classes:
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# Figure
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fig = plt.figure(c_name + " IoU")
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for i, name in enumerate(list_of_names):
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plt.plot(
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all_pred_epochs[i],
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all_class_IoUs[i][:, c_i],
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linewidth=1,
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label=name,
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)
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plt.xlabel("epochs")
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plt.ylabel("IoU")
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# Set limits for y axis
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# plt.ylim(0.8, 1)
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# Display legends and title
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plt.legend(loc=4)
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# Customize the graph
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ax = fig.gca()
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ax.grid(linestyle="-.", which="both")
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# ax.set_yticks(np.arange(0.8, 1.02, 0.02))
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# Show all
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plt.show()
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def compare_convergences_classif(list_of_paths, list_of_labels=None):
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# Parameters
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# **********
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smooth_n = 12
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if list_of_labels is None:
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list_of_labels = [str(i) for i in range(len(list_of_paths))]
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# Read Logs
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# *********
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all_pred_epochs = []
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all_val_OA = []
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all_vote_OA = []
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all_vote_confs = []
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for path in list_of_paths:
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# Load parameters
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config = Config()
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config.load(list_of_paths[0])
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# Get the number of classes
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n_class = config.num_classes
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# Load epochs
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epochs, _, _, _, _, _ = load_training_results(path)
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first_e = np.min(epochs)
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# Get validation confusions
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file = join(path, "val_confs.txt")
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val_C1 = load_confusions(file, n_class)
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val_PRE, val_REC, val_F1, val_IoU, val_ACC = smooth_metrics(
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val_C1, smooth_n=smooth_n
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)
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# Get vote confusions
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file = join(path, "vote_confs.txt")
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if exists(file):
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vote_C2 = load_confusions(file, n_class)
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vote_PRE, vote_REC, vote_F1, vote_IoU, vote_ACC = smooth_metrics(
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vote_C2, smooth_n=2
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)
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else:
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vote_C2 = val_C1
|
|
vote_PRE, vote_REC, vote_F1, vote_IoU, vote_ACC = (
|
|
val_PRE,
|
|
val_REC,
|
|
val_F1,
|
|
val_IoU,
|
|
val_ACC,
|
|
)
|
|
|
|
# Aggregate results
|
|
all_pred_epochs += [np.array([i + first_e for i in range(len(val_ACC))])]
|
|
all_val_OA += [val_ACC]
|
|
all_vote_OA += [vote_ACC]
|
|
all_vote_confs += [vote_C2]
|
|
|
|
print()
|
|
|
|
# Best scores
|
|
# ***********
|
|
|
|
for i, label in enumerate(list_of_labels):
|
|
print("\n" + label + "\n" + "*" * len(label) + "\n")
|
|
print(list_of_paths[i])
|
|
|
|
best_epoch = np.argmax(all_vote_OA[i])
|
|
print(
|
|
"Best Accuracy : {:.1f} % (epoch {:d})".format(
|
|
100 * all_vote_OA[i][best_epoch], best_epoch
|
|
)
|
|
)
|
|
|
|
confs = all_vote_confs[i]
|
|
|
|
"""
|
|
s = ''
|
|
for cc in confs[best_epoch]:
|
|
for c in cc:
|
|
s += '{:.0f} '.format(c)
|
|
s += '\n'
|
|
print(s)
|
|
"""
|
|
|
|
TP_plus_FN = np.sum(confs, axis=-1, keepdims=True)
|
|
class_avg_confs = confs.astype(np.float32) / TP_plus_FN.astype(np.float32)
|
|
diags = np.diagonal(class_avg_confs, axis1=-2, axis2=-1)
|
|
class_avg_ACC = np.sum(diags, axis=-1) / np.sum(class_avg_confs, axis=(-1, -2))
|
|
|
|
print("Corresponding mAcc : {:.1f} %".format(100 * class_avg_ACC[best_epoch]))
|
|
|
|
# Plots
|
|
# *****
|
|
|
|
for fig_name, OA in zip(["Validation", "Vote"], [all_val_OA, all_vote_OA]):
|
|
# Figure
|
|
fig = plt.figure(fig_name)
|
|
for i, label in enumerate(list_of_labels):
|
|
plt.plot(all_pred_epochs[i], OA[i], linewidth=1, label=label)
|
|
plt.xlabel("epochs")
|
|
plt.ylabel(fig_name + " Accuracy")
|
|
|
|
# Set limits for y axis
|
|
# plt.ylim(0.55, 0.95)
|
|
|
|
# Display legends and title
|
|
plt.legend(loc=4)
|
|
|
|
# Customize the graph
|
|
ax = fig.gca()
|
|
ax.grid(linestyle="-.", which="both")
|
|
# ax.set_yticks(np.arange(0.8, 1.02, 0.02))
|
|
|
|
# for i, label in enumerate(list_of_labels):
|
|
# print(label, np.max(all_train_OA[i]), np.max(all_val_OA[i]))
|
|
|
|
# Show all
|
|
plt.show()
|
|
|
|
|
|
def compare_convergences_SLAM(dataset, list_of_paths, list_of_names=None):
|
|
# Parameters
|
|
# **********
|
|
|
|
smooth_n = 10
|
|
|
|
if list_of_names is None:
|
|
list_of_names = [str(i) for i in range(len(list_of_paths))]
|
|
|
|
# Read Logs
|
|
# *********
|
|
|
|
all_pred_epochs = []
|
|
all_val_mIoUs = []
|
|
all_val_class_IoUs = []
|
|
all_subpart_mIoUs = []
|
|
all_subpart_class_IoUs = []
|
|
|
|
# Load parameters
|
|
config = Config()
|
|
config.load(list_of_paths[0])
|
|
|
|
class_list = [
|
|
dataset.label_to_names[label]
|
|
for label in dataset.label_values
|
|
if label not in dataset.ignored_labels
|
|
]
|
|
|
|
s = "{:^6}|".format("mean")
|
|
for c in class_list:
|
|
s += "{:^6}".format(c[:4])
|
|
print(s)
|
|
print(6 * "-" + "|" + 6 * config.num_classes * "-")
|
|
for path in list_of_paths:
|
|
# Get validation IoUs
|
|
nc_model = dataset.num_classes - len(dataset.ignored_labels)
|
|
file = join(path, "val_IoUs.txt")
|
|
val_IoUs = load_single_IoU(file, nc_model)
|
|
|
|
# Get Subpart IoUs
|
|
file = join(path, "subpart_IoUs.txt")
|
|
subpart_IoUs = load_single_IoU(file, nc_model)
|
|
|
|
# Get mean IoU
|
|
val_class_IoUs, val_mIoUs = IoU_class_metrics(val_IoUs, smooth_n)
|
|
subpart_class_IoUs, subpart_mIoUs = IoU_class_metrics(subpart_IoUs, smooth_n)
|
|
|
|
# Aggregate results
|
|
all_pred_epochs += [np.array([i for i in range(len(val_IoUs))])]
|
|
all_val_mIoUs += [val_mIoUs]
|
|
all_val_class_IoUs += [val_class_IoUs]
|
|
all_subpart_mIoUs += [subpart_mIoUs]
|
|
all_subpart_class_IoUs += [subpart_class_IoUs]
|
|
|
|
s = "{:^6.1f}|".format(100 * subpart_mIoUs[-1])
|
|
for IoU in subpart_class_IoUs[-1]:
|
|
s += "{:^6.1f}".format(100 * IoU)
|
|
print(s)
|
|
|
|
print(6 * "-" + "|" + 6 * config.num_classes * "-")
|
|
for snap_IoUs in all_val_class_IoUs:
|
|
if len(snap_IoUs) > 0:
|
|
s = "{:^6.1f}|".format(100 * np.mean(snap_IoUs[-1]))
|
|
for IoU in snap_IoUs[-1]:
|
|
s += "{:^6.1f}".format(100 * IoU)
|
|
else:
|
|
s = "{:^6s}".format("-")
|
|
for _ in range(config.num_classes):
|
|
s += "{:^6s}".format("-")
|
|
print(s)
|
|
|
|
# Plots
|
|
# *****
|
|
|
|
# Figure
|
|
fig = plt.figure("mIoUs")
|
|
for i, name in enumerate(list_of_names):
|
|
p = plt.plot(
|
|
all_pred_epochs[i], all_subpart_mIoUs[i], "--", linewidth=1, label=name
|
|
)
|
|
plt.plot(
|
|
all_pred_epochs[i], all_val_mIoUs[i], linewidth=1, color=p[-1].get_color()
|
|
)
|
|
plt.xlabel("epochs")
|
|
plt.ylabel("IoU")
|
|
|
|
# Set limits for y axis
|
|
# plt.ylim(0.55, 0.95)
|
|
|
|
# Display legends and title
|
|
plt.legend(loc=4)
|
|
|
|
# Customize the graph
|
|
ax = fig.gca()
|
|
ax.grid(linestyle="-.", which="both")
|
|
# ax.set_yticks(np.arange(0.8, 1.02, 0.02))
|
|
|
|
displayed_classes = [0, 1, 2, 3, 4, 5, 6, 7]
|
|
# displayed_classes = []
|
|
for c_i, c_name in enumerate(class_list):
|
|
if c_i in displayed_classes:
|
|
# Figure
|
|
fig = plt.figure(c_name + " IoU")
|
|
for i, name in enumerate(list_of_names):
|
|
plt.plot(
|
|
all_pred_epochs[i],
|
|
all_val_class_IoUs[i][:, c_i],
|
|
linewidth=1,
|
|
label=name,
|
|
)
|
|
plt.xlabel("epochs")
|
|
plt.ylabel("IoU")
|
|
|
|
# Set limits for y axis
|
|
# plt.ylim(0.8, 1)
|
|
|
|
# Display legends and title
|
|
plt.legend(loc=4)
|
|
|
|
# Customize the graph
|
|
ax = fig.gca()
|
|
ax.grid(linestyle="-.", which="both")
|
|
# ax.set_yticks(np.arange(0.8, 1.02, 0.02))
|
|
|
|
# Show all
|
|
plt.show()
|
|
|
|
|
|
# ----------------------------------------------------------------------------------------------------------------------
|
|
#
|
|
# Experiments
|
|
# \*****************/
|
|
#
|
|
|
|
|
|
def experiment_name_1():
|
|
"""
|
|
In this function you choose the results you want to plot together, to compare them as an experiment.
|
|
Just return the list of log paths (like 'results/Log_2020-04-04_10-04-42' for example), and the associated names
|
|
of these logs.
|
|
Below an example of how to automatically gather all logs between two dates, and name them.
|
|
"""
|
|
|
|
# Using the dates of the logs, you can easily gather consecutive ones. All logs should be of the same dataset.
|
|
start = "Log_2020-04-22_11-52-58"
|
|
end = "Log_2023-07-29_12-40-27"
|
|
|
|
# Name of the result path
|
|
res_path = "results"
|
|
|
|
# Gather logs and sort by date
|
|
logs = np.sort(
|
|
[join(res_path, l) for l in listdir_str(res_path) if start <= l <= end]
|
|
)
|
|
|
|
# Give names to the logs (for plot legends)
|
|
logs_names = ["name_log_1", "name_log_2", "name_log_3", "name_log_4"]
|
|
|
|
# safe check log names
|
|
logs_names = np.array(logs_names[: len(logs)])
|
|
|
|
return logs, logs_names
|
|
|
|
|
|
def experiment_name_2():
|
|
"""
|
|
In this function you choose the results you want to plot together, to compare them as an experiment.
|
|
Just return the list of log paths (like 'results/Log_2020-04-04_10-04-42' for example), and the associated names
|
|
of these logs.
|
|
Below an example of how to automatically gather all logs between two dates, and name them.
|
|
"""
|
|
|
|
# Using the dates of the logs, you can easily gather consecutive ones. All logs should be of the same dataset.
|
|
start = "Log_2020-04-22_11-52-58"
|
|
end = "Log_2020-05-22_11-52-58"
|
|
|
|
# Name of the result path
|
|
res_path = "results"
|
|
|
|
# Gather logs and sort by date
|
|
logs = np.sort(
|
|
[join(res_path, l) for l in listdir_str(res_path) if start <= l <= end]
|
|
)
|
|
|
|
# Optionally add a specific log at a specific place in the log list
|
|
logs = logs.astype("<U50")
|
|
logs = np.insert(logs, 0, "results/Log_2020-04-04_10-04-42")
|
|
|
|
# Give names to the logs (for plot legends)
|
|
logs_names = ["name_log_inserted", "name_log_1", "name_log_2", "name_log_3"]
|
|
|
|
# safe check log names
|
|
logs_names = np.array(logs_names[: len(logs)])
|
|
|
|
return logs, logs_names
|
|
|
|
|
|
# ----------------------------------------------------------------------------------------------------------------------
|
|
#
|
|
# Main Call
|
|
# \***************/
|
|
#
|
|
|
|
if __name__ == "__main__":
|
|
######################################################
|
|
# Choose a list of log to plot together for comparison
|
|
######################################################
|
|
|
|
# My logs: choose the logs to show
|
|
logs, logs_names = experiment_name_1()
|
|
|
|
################
|
|
# Plot functions
|
|
################
|
|
|
|
# Check that all logs are of the same dataset. Different object can be compared
|
|
plot_dataset = None
|
|
config = None
|
|
for log in logs:
|
|
config = Config()
|
|
config.load(log)
|
|
if "ShapeNetPart" in config.dataset:
|
|
this_dataset = "ShapeNetPart"
|
|
else:
|
|
this_dataset = config.dataset
|
|
if plot_dataset:
|
|
if plot_dataset == this_dataset:
|
|
continue
|
|
else:
|
|
raise ValueError("All logs must share the same dataset to be compared")
|
|
else:
|
|
plot_dataset = this_dataset
|
|
|
|
# Plot the training loss and accuracy
|
|
compare_trainings(logs, logs_names)
|
|
|
|
# Plot the validation
|
|
if config.dataset_task == "classification":
|
|
compare_convergences_classif(logs, logs_names)
|
|
elif config.dataset_task == "cloud_segmentation":
|
|
if config.dataset.startswith("S3DIS"):
|
|
dataset = S3DISDataset(config, load_data=False)
|
|
compare_convergences_segment(dataset, logs, logs_names)
|
|
elif config.dataset_task == "slam_segmentation":
|
|
if config.dataset.startswith("SemanticKitti"):
|
|
dataset = SemanticKittiDataset(config)
|
|
compare_convergences_SLAM(dataset, logs, logs_names)
|
|
else:
|
|
raise ValueError("Unsupported dataset : " + plot_dataset)
|